High Precision Location Estimation in Mountainous Areas Using GPS

被引:3
作者
Kunisada, Yugo [1 ]
Premachandra, Chinthaka [1 ]
机构
[1] Shibaura Inst Technol, Grad Sch Engn & Sci, Dept Elect Engn & Comp Sci, Tokyo 1358548, Japan
基金
日本学术振兴会;
关键词
GPS; GNSS; data science; location estimation;
D O I
10.3390/s22031149
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Outdoor recreation has become popular in recent years, against the backdrop of the new coronavirus epidemic that started in 2020. Mountaineering, in particular, has become a popular pastime for many people as an easy way to experience nature. However, the number of mountaineering accidents is increasing, owing to the inadequate knowledge and equipment for beginners. In particular, the lack of map-reading skills and experience often leads to the selection of wrong trails. The smartphones used for precise location information obtain correction information from radio waves from a base station, and the accuracy of using only the GPS in mountainous areas without radio waves is questionable. In general, the GPS position correction methods in the literature for such situations include complex processing of the GPS radio waves. Some of these methods have been proposed with complex hardware and are difficult to implement with portable hardware. In this study, we develop and demonstrate a method for obtaining accurate location information using GPS without the error correction of radio waves, even in mountainous areas. The multipath is the reason for most of the GPS errors in the mountains. In the mountains, depending on the locations, the correct GPS location can also be received. In the proposed method, the correct GPS data are used to detect the incorrect GPS locations. We present an experimental method for estimating the interrelationship between the GPS longitude and latitude data. Additionally, we demonstrate the effectiveness of our method by showing that the experimental mountain location data presented in this paper are more accurate than the GPS data alone.
引用
收藏
页数:11
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